The convenience offered by the Internet accelerates the evolution of fraudulent behavior during facilitating the rapid development of online payment services. Fraudsters can change their behavior patterns frequently and at a low cost in the online space, allowing them to evade regulatory oversight. This poses a significant challenge for meticulously trained learning-based security applications for fraud detection and can lead to serious social security risks. Most of them depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. To stay ahead of the rapidly evolving fraud, researchers have been exploring models with low latency and fast response capabilities to effectively combat fraudulent behavior. Unfortunately, the evolving fraud is not only reflected in the drift of their superimposed risk features but also in the openness of their category. The interweaving of open-category and concept-drift accelerates the process of existing security methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by theanchoring effecton the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank.

In data-driven anti-fraud engineering for online payment services, the integration of proper function modules is an effective way to further improve detection performance by overcoming the inability of single-function methods to cope with complex and varied frauds. However, a qualified integration is really inaccessible under multiple demanding requirements, i.e., improving detection performance, ensuring decision explainability, and limiting processing latency and computing consumption. In this work, we propose a qualified integration system, named CAeSaR, that can simultaneously meet all of the above requirements. This satisfactory result is achieved by the cooperation of two innovative techniques. The first is a novel three-way taxonomy of function division, called TRTPT, according to the temporal positions of transactions relative to a reference fraudulent transaction. Based on TRTPT, CAeSaR can introduce three kinds of anti-fraud function modules which collaboratively cover all types of frauds theoretically. The second is an effective integration scheme, called TELSI. It generates the candidate decision strategies by combining the judgments of three function modules by only two simple logical connectives, which essentially ensures the decision explainability. Particularly, TELSI can assign the most effective decision strategy to the corresponding transaction adaptively by a devised stacking-based multi-classification. The advantages of CAeSaR are validated in practice over real-life data from a prestigious bank.